Text-to-Image
Diffusers
TensorBoard
StableDiffusionPipeline
stable-diffusion
stable-diffusion-diffusers
textual_inversion
Instructions to use anic87/textual_inversion_32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use anic87/textual_inversion_32 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", dtype=torch.bfloat16, device_map="cuda") pipe.load_textual_inversion("anic87/textual_inversion_32") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
| { | |
| "_class_name": "StableDiffusionPipeline", | |
| "_diffusers_version": "0.17.0.dev0", | |
| "feature_extractor": [ | |
| "transformers", | |
| "CLIPFeatureExtractor" | |
| ], | |
| "requires_safety_checker": true, | |
| "safety_checker": [ | |
| "stable_diffusion", | |
| "StableDiffusionSafetyChecker" | |
| ], | |
| "scheduler": [ | |
| "diffusers", | |
| "PNDMScheduler" | |
| ], | |
| "text_encoder": [ | |
| "transformers", | |
| "CLIPTextModel" | |
| ], | |
| "tokenizer": [ | |
| "transformers", | |
| "CLIPTokenizer" | |
| ], | |
| "unet": [ | |
| "diffusers", | |
| "UNet2DConditionModel" | |
| ], | |
| "vae": [ | |
| "diffusers", | |
| "AutoencoderKL" | |
| ] | |
| } | |